TDBSCAN: Spatiotemporal Density Clustering

Trajectory data generated from personal or vehicle use of GPS devices can be utilized for travel analysis and traffic information service, whereas trip segmentation is a key step toward the semantic labelling of the trajectories. Two issues are difficult to deal with by the traditional density-based algorithms, i. e. multiple stops at the same spatial location with different visit times and non-consecutive point sequence for stop definition due to signal drifting. This article aims to develop a modified density-based clustering algorithm, named T-DBSCAN, by considering the time-sequential characteristics of the GPS points along a trajectory. Two new premises (i.e. state continuity within a single stop and temporal disjuncture among stops) were proposed as a theoretical basis for regulating the trajectory point selection in clustering. An empirical test was performed using a GPS-based personal travel dataset collected in the city of Shanghai to compare T-DBSCAN against DBSCAN. The results indicated that T-DBSCAN effectively improved both accuracy and computational speed in trajectory segmentation.